where \(p(y_j|y<j, z, z_{c^{\prime }}, x)=g(y_{j-1}, s_{j-1}, e_j)\) , \(g(\cdot )\)  is an attention-based feed forward model, \(e_j=\sum _i \alpha _{ji}h_i^{x}\)  is the context vector and \(s_{j-1}\)  is the hidden state of the decoder. We obtain \(g(\cdot )\)  and \(e_j\)  the same way as [1]} ([1]}). Whereas our decoder differs from [1]} ([1]}) in that our model integrates the context-aware latent variable \(z_{c^{\prime }}\)  and semantic latent variable \(z\)  in the computation of \(s_j=\mathrm {GRU}([E_{yj};s_{j-1},z,z_{c^{\prime }}])\) , where \(E_{yj}\)  is the word embeddings of target words.
